Approximating the Manifold Structure of Attributed Incentive Salience from Large Scale Behavioural Data. A Representation Learning Approach Based on Artificial Neural Networks. (arXiv:2108.01724v2 [cs.LG] UPDATED)
Incentive salience attribution can be understood as a psychobiological
mechanism ascribing relevance to potentially rewarding objects and actions.
Despite being an important component of the motivational process guiding our
everyday behaviour its study in naturalistic contexts is not straightforward.
Here we propose a methodology based on artificial neural networks (ANNs) for
approximating latent states produced by this process in situations where large
volumes of behavioural data are available but no experimental control is
possible. Leveraging knowledge derived from theoretical and computational
accounts of incentive salience attribution we designed an ANN for estimating
duration and intensity of future interactions between individuals and a series
of video games in a large-scale ($N> 3 \times 10^6$) longitudinal dataset. We
found video games to be the ideal context for developing such methodology due
to their reliance on reward mechanics and their ability to provide ecologically
robust behavioural measures at scale. When compared to competing approaches our
methodology produces representations that are better suited for predicting the
intensity future behaviour and approximating some functional properties of
attributed incentive salience. We discuss our findings with reference to the
adopted theoretical and computational frameworks and suggest how our
methodology could be an initial step for estimating attributed incentive
salience in large scale behavioural studies.
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